Overview

Dataset statistics

Number of variables15
Number of observations3591
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory410.4 KiB
Average record size in memory117.0 B

Variable types

Numeric9
Categorical6

Warnings

prev_call_duration is highly correlated with subs_depositHigh correlation
subs_deposit is highly correlated with prev_call_durationHigh correlation
contact_month is highly correlated with contact_weekHigh correlation
contact_week is highly correlated with contact_monthHigh correlation
prev_call_duration is highly correlated with subs_depositHigh correlation
subs_deposit is highly correlated with prev_call_durationHigh correlation
contact_month is highly correlated with contact_weekHigh correlation
contact_week is highly correlated with contact_monthHigh correlation
contact_month is highly correlated with contact_weekHigh correlation
contact_week is highly correlated with contact_monthHigh correlation
contact_weekday is highly correlated with contact_day and 3 other fieldsHigh correlation
subs_deposit is highly correlated with prev_call_duration and 1 other fieldsHigh correlation
prev_call_duration is highly correlated with subs_depositHigh correlation
cpi is highly correlated with subs_deposit and 3 other fieldsHigh correlation
job is highly correlated with educationHigh correlation
education is highly correlated with jobHigh correlation
contact_day is highly correlated with contact_weekday and 3 other fieldsHigh correlation
contact_month is highly correlated with contact_weekday and 2 other fieldsHigh correlation
client_id is highly correlated with contact_weekday and 4 other fieldsHigh correlation
contact_week is highly correlated with contact_weekday and 4 other fieldsHigh correlation
client_id has unique values Unique
num_contacts_prev has 2927 (81.5%) zeros Zeros

Reproduction

Analysis started2022-04-14 02:12:03.515238
Analysis finished2022-04-14 02:17:52.927143
Duration5 minutes and 49.41 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

client_id
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct3591
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22151.05904
Minimum17
Maximum41186
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.2 KiB
2022-04-14T12:17:53.004449image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile2520.5
Q111942
median22807
Q332774.5
95-th percentile39702.5
Maximum41186
Range41169
Interquartile range (IQR)20832.5

Descriptive statistics

Standard deviation12074.92553
Coefficient of variation (CV)0.5451173018
Kurtosis-1.216305068
Mean22151.05904
Median Absolute Deviation (MAD)10260
Skewness-0.1264467109
Sum79544453
Variance145803826.6
MonotonicityNot monotonic
2022-04-14T12:17:53.124473image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
410201
 
< 0.1%
54791
 
< 0.1%
242311
 
< 0.1%
208121
 
< 0.1%
140051
 
< 0.1%
313561
 
< 0.1%
245081
 
< 0.1%
407001
 
< 0.1%
355471
 
< 0.1%
82821
 
< 0.1%
Other values (3581)3581
99.7%
ValueCountFrequency (%)
171
< 0.1%
531
< 0.1%
681
< 0.1%
1011
< 0.1%
1151
< 0.1%
1391
< 0.1%
1801
< 0.1%
1921
< 0.1%
2381
< 0.1%
2461
< 0.1%
ValueCountFrequency (%)
411861
< 0.1%
411851
< 0.1%
411811
< 0.1%
411671
< 0.1%
411631
< 0.1%
411331
< 0.1%
411311
< 0.1%
411181
< 0.1%
411031
< 0.1%
410931
< 0.1%

age_bracket
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.2 KiB
2
1971 
3
1386 
4
 
117
1
 
117

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3591
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row4
3rd row3
4th row2
5th row1

Common Values

ValueCountFrequency (%)
21971
54.9%
31386
38.6%
4117
 
3.3%
1117
 
3.3%

Length

2022-04-14T12:17:53.320529image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-14T12:17:53.378722image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
21971
54.9%
31386
38.6%
4117
 
3.3%
1117
 
3.3%

Most occurring characters

ValueCountFrequency (%)
21971
54.9%
31386
38.6%
4117
 
3.3%
1117
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3591
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
21971
54.9%
31386
38.6%
4117
 
3.3%
1117
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
Common3591
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
21971
54.9%
31386
38.6%
4117
 
3.3%
1117
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII3591
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
21971
54.9%
31386
38.6%
4117
 
3.3%
1117
 
3.3%

job
Real number (ℝ≥0)

HIGH CORRELATION

Distinct7
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.342522974
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.2 KiB
2022-04-14T12:17:53.432474image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q35
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.315546149
Coefficient of variation (CV)0.6927539965
Kurtosis-1.332489353
Mean3.342522974
Median Absolute Deviation (MAD)1
Skewness0.4557118303
Sum12003
Variance5.361753967
MonotonicityNot monotonic
2022-04-14T12:17:53.503322image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
11256
35.0%
7694
19.3%
2573
16.0%
5415
 
11.6%
4409
 
11.4%
3144
 
4.0%
6100
 
2.8%
ValueCountFrequency (%)
11256
35.0%
2573
16.0%
3144
 
4.0%
4409
 
11.4%
5415
 
11.6%
6100
 
2.8%
7694
19.3%
ValueCountFrequency (%)
7694
19.3%
6100
 
2.8%
5415
 
11.6%
4409
 
11.4%
3144
 
4.0%
2573
16.0%
11256
35.0%

marital
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.2 KiB
1.0
2141 
2.0
1051 
3.0
399 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10773
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row1.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.02141
59.6%
2.01051
29.3%
3.0399
 
11.1%

Length

2022-04-14T12:17:53.676444image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-14T12:17:53.734548image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.02141
59.6%
2.01051
29.3%
3.0399
 
11.1%

Most occurring characters

ValueCountFrequency (%)
.3591
33.3%
03591
33.3%
12141
19.9%
21051
 
9.8%
3399
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number7182
66.7%
Other Punctuation3591
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03591
50.0%
12141
29.8%
21051
 
14.6%
3399
 
5.6%
Other Punctuation
ValueCountFrequency (%)
.3591
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10773
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.3591
33.3%
03591
33.3%
12141
19.9%
21051
 
9.8%
3399
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII10773
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.3591
33.3%
03591
33.3%
12141
19.9%
21051
 
9.8%
3399
 
3.7%

education
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.2 KiB
1.0
1199 
2.0
1052 
3.0
850 
4.0
486 
6.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10773
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row2.0
3rd row1.0
4th row3.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.01199
33.4%
2.01052
29.3%
3.0850
23.7%
4.0486
13.5%
6.04
 
0.1%

Length

2022-04-14T12:17:53.886968image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-14T12:17:53.946155image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.01199
33.4%
2.01052
29.3%
3.0850
23.7%
4.0486
13.5%
6.04
 
0.1%

Most occurring characters

ValueCountFrequency (%)
.3591
33.3%
03591
33.3%
11199
 
11.1%
21052
 
9.8%
3850
 
7.9%
4486
 
4.5%
64
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number7182
66.7%
Other Punctuation3591
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03591
50.0%
11199
 
16.7%
21052
 
14.6%
3850
 
11.8%
4486
 
6.8%
64
 
0.1%
Other Punctuation
ValueCountFrequency (%)
.3591
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10773
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.3591
33.3%
03591
33.3%
11199
 
11.1%
21052
 
9.8%
3850
 
7.9%
4486
 
4.5%
64
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII10773
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.3591
33.3%
03591
33.3%
11199
 
11.1%
21052
 
9.8%
3850
 
7.9%
4486
 
4.5%
64
 
< 0.1%

has_housing_loan
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.2 KiB
1.0
1939 
0.0
1652 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10773
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.01939
54.0%
0.01652
46.0%

Length

2022-04-14T12:17:54.099083image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-14T12:17:54.155227image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.01939
54.0%
0.01652
46.0%

Most occurring characters

ValueCountFrequency (%)
05243
48.7%
.3591
33.3%
11939
 
18.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number7182
66.7%
Other Punctuation3591
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
05243
73.0%
11939
 
27.0%
Other Punctuation
ValueCountFrequency (%)
.3591
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10773
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
05243
48.7%
.3591
33.3%
11939
 
18.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10773
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
05243
48.7%
.3591
33.3%
11939
 
18.0%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.2 KiB
0.0
3071 
1.0
520 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10773
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03071
85.5%
1.0520
 
14.5%

Length

2022-04-14T12:17:54.288964image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-14T12:17:54.344376image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.03071
85.5%
1.0520
 
14.5%

Most occurring characters

ValueCountFrequency (%)
06662
61.8%
.3591
33.3%
1520
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number7182
66.7%
Other Punctuation3591
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06662
92.8%
1520
 
7.2%
Other Punctuation
ValueCountFrequency (%)
.3591
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10773
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06662
61.8%
.3591
33.3%
1520
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII10773
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06662
61.8%
.3591
33.3%
1520
 
4.8%

prev_call_duration
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct938
Distinct (%)26.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean323.4575327
Minimum2
Maximum1337
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.2 KiB
2022-04-14T12:17:54.411032image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile49
Q1126
median227
Q3436.5
95-th percentile928
Maximum1337
Range1335
Interquartile range (IQR)310.5

Descriptive statistics

Standard deviation274.3864898
Coefficient of variation (CV)0.848292162
Kurtosis1.420470042
Mean323.4575327
Median Absolute Deviation (MAD)126
Skewness1.390693054
Sum1161536
Variance75287.94576
MonotonicityNot monotonic
2022-04-14T12:17:54.527847image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15919
 
0.5%
7219
 
0.5%
7617
 
0.5%
16416
 
0.4%
13116
 
0.4%
16116
 
0.4%
16516
 
0.4%
15716
 
0.4%
15615
 
0.4%
10015
 
0.4%
Other values (928)3426
95.4%
ValueCountFrequency (%)
21
 
< 0.1%
31
 
< 0.1%
43
0.1%
52
 
0.1%
65
0.1%
75
0.1%
82
 
0.1%
91
 
< 0.1%
103
0.1%
113
0.1%
ValueCountFrequency (%)
13371
< 0.1%
13361
< 0.1%
13292
0.1%
13281
< 0.1%
13271
< 0.1%
13212
0.1%
13191
< 0.1%
13171
< 0.1%
13131
< 0.1%
13091
< 0.1%

num_contacts_prev
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2553606238
Minimum0
Maximum6
Zeros2927
Zeros (%)81.5%
Negative0
Negative (%)0.0%
Memory size28.2 KiB
2022-04-14T12:17:54.622154image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.623771061
Coefficient of variation (CV)2.442706521
Kurtosis12.73133201
Mean0.2553606238
Median Absolute Deviation (MAD)0
Skewness3.166113889
Sum917
Variance0.3890903365
MonotonicityNot monotonic
2022-04-14T12:17:54.692604image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
02927
81.5%
1489
 
13.6%
2116
 
3.2%
346
 
1.3%
48
 
0.2%
54
 
0.1%
61
 
< 0.1%
ValueCountFrequency (%)
02927
81.5%
1489
 
13.6%
2116
 
3.2%
346
 
1.3%
48
 
0.2%
54
 
0.1%
61
 
< 0.1%
ValueCountFrequency (%)
61
 
< 0.1%
54
 
0.1%
48
 
0.2%
346
 
1.3%
2116
 
3.2%
1489
 
13.6%
02927
81.5%

cpi
Real number (ℝ≥0)

HIGH CORRELATION

Distinct23
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.48118574
Minimum92.201
Maximum94.465
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.2 KiB
2022-04-14T12:17:54.779227image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum92.201
5-th percentile92.431
Q192.963
median93.444
Q393.994
95-th percentile94.465
Maximum94.465
Range2.264
Interquartile range (IQR)1.031

Descriptive statistics

Standard deviation0.6097840083
Coefficient of variation (CV)0.006523066684
Kurtosis-0.9449005877
Mean93.48118574
Median Absolute Deviation (MAD)0.55
Skewness-0.1791018275
Sum335690.938
Variance0.3718365368
MonotonicityNot monotonic
2022-04-14T12:17:54.866481image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
93.994512
14.3%
92.893489
13.6%
93.918488
13.6%
93.444387
10.8%
94.465315
8.8%
93.2281
7.8%
93.075261
7.3%
92.201122
 
3.4%
92.963107
 
3.0%
92.43172
 
2.0%
Other values (13)557
15.5%
ValueCountFrequency (%)
92.201122
 
3.4%
92.37939
 
1.1%
92.43172
 
2.0%
92.46933
 
0.9%
92.64958
 
1.6%
92.71326
 
0.7%
92.84347
 
1.3%
92.893489
13.6%
92.963107
 
3.0%
93.075261
7.3%
ValueCountFrequency (%)
94.465315
8.8%
94.21569
 
1.9%
94.19954
 
1.5%
94.05539
 
1.1%
94.02746
 
1.3%
93.994512
14.3%
93.918488
13.6%
93.87646
 
1.3%
93.79817
 
0.5%
93.74932
 
0.9%

subs_deposit
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.2 KiB
0
2232 
1
1359 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3591
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
02232
62.2%
11359
37.8%

Length

2022-04-14T12:17:55.032154image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-14T12:17:55.087901image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
02232
62.2%
11359
37.8%

Most occurring characters

ValueCountFrequency (%)
02232
62.2%
11359
37.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3591
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02232
62.2%
11359
37.8%

Most occurring scripts

ValueCountFrequency (%)
Common3591
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02232
62.2%
11359
37.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII3591
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02232
62.2%
11359
37.8%

contact_month
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct7
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.875800613
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.2 KiB
2022-04-14T12:17:55.132229image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.009351887
Coefficient of variation (CV)0.5184353087
Kurtosis-1.263904962
Mean3.875800613
Median Absolute Deviation (MAD)2
Skewness0.1170633473
Sum13918
Variance4.037495006
MonotonicityNot monotonic
2022-04-14T12:17:55.201186image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2660
18.4%
4540
15.0%
1516
14.4%
7501
14.0%
6461
12.8%
3458
12.8%
5455
12.7%
ValueCountFrequency (%)
1516
14.4%
2660
18.4%
3458
12.8%
4540
15.0%
5455
12.7%
6461
12.8%
7501
14.0%
ValueCountFrequency (%)
7501
14.0%
6461
12.8%
5455
12.7%
4540
15.0%
3458
12.8%
2660
18.4%
1516
14.4%

contact_day
Real number (ℝ≥0)

HIGH CORRELATION

Distinct10
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.629072682
Minimum3
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.2 KiB
2022-04-14T12:17:55.280683image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile4
Q15
median6
Q38
95-th percentile11
Maximum12
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.121774945
Coefficient of variation (CV)0.3200711543
Kurtosis-0.2461639541
Mean6.629072682
Median Absolute Deviation (MAD)1
Skewness0.6943090981
Sum23805
Variance4.501928917
MonotonicityNot monotonic
2022-04-14T12:17:55.352012image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
51047
29.2%
7590
16.4%
8555
15.5%
6461
12.8%
11339
 
9.4%
4293
 
8.2%
398
 
2.7%
993
 
2.6%
1089
 
2.5%
1226
 
0.7%
ValueCountFrequency (%)
398
 
2.7%
4293
 
8.2%
51047
29.2%
6461
12.8%
7590
16.4%
8555
15.5%
993
 
2.6%
1089
 
2.5%
11339
 
9.4%
1226
 
0.7%
ValueCountFrequency (%)
1226
 
0.7%
11339
 
9.4%
1089
 
2.5%
993
 
2.6%
8555
15.5%
7590
16.4%
6461
12.8%
51047
29.2%
4293
 
8.2%
398
 
2.7%

contact_week
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct16
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.77276525
Minimum1
Maximum28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.7 KiB
2022-04-14T12:17:55.427613image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q16
median14
Q323
95-th percentile27
Maximum28
Range27
Interquartile range (IQR)17

Descriptive statistics

Standard deviation8.643006035
Coefficient of variation (CV)0.6275432624
Kurtosis-1.247940309
Mean13.77276525
Median Absolute Deviation (MAD)8
Skewness0.1294023327
Sum49458
Variance74.70155333
MonotonicityNot monotonic
2022-04-14T12:17:55.512078image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
6583
16.2%
14488
13.6%
10417
11.6%
27404
11.3%
23358
10.0%
1323
9.0%
19241
6.7%
18214
 
6.0%
2193
 
5.4%
2897
 
2.7%
Other values (6)273
7.6%
ValueCountFrequency (%)
1323
9.0%
2193
 
5.4%
577
 
2.1%
6583
16.2%
931
 
0.9%
10417
11.6%
1110
 
0.3%
14488
13.6%
1552
 
1.4%
18214
 
6.0%
ValueCountFrequency (%)
2897
 
2.7%
27404
11.3%
2470
 
1.9%
23358
10.0%
2233
 
0.9%
19241
6.7%
18214
6.0%
1552
 
1.4%
14488
13.6%
1110
 
0.3%

contact_weekday
Real number (ℝ≥0)

HIGH CORRELATION

Distinct7
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.817878028
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.2 KiB
2022-04-14T12:17:55.588815image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q35
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.94054326
Coefficient of variation (CV)0.508277961
Kurtosis-1.002370757
Mean3.817878028
Median Absolute Deviation (MAD)1
Skewness-0.1041994699
Sum13710
Variance3.765708142
MonotonicityNot monotonic
2022-04-14T12:17:55.660489image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1843
23.5%
4828
23.1%
5708
19.7%
3480
13.4%
7387
10.8%
6294
 
8.2%
251
 
1.4%
ValueCountFrequency (%)
1843
23.5%
251
 
1.4%
3480
13.4%
4828
23.1%
5708
19.7%
6294
 
8.2%
7387
10.8%
ValueCountFrequency (%)
7387
10.8%
6294
 
8.2%
5708
19.7%
4828
23.1%
3480
13.4%
251
 
1.4%
1843
23.5%

Interactions

2022-04-14T12:12:07.625038image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:12:47.981349image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:13:13.038988image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:13:38.636331image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:14:04.430730image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:14:29.548307image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:14:55.436619image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:15:21.884756image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:15:48.291331image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:16:13.292086image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:16:24.187642image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:16:24.278185image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:16:24.373555image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:16:24.465973image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:16:24.555749image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:16:24.646601image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:16:24.733775image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:16:24.822329image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:16:24.918473image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:16:38.423004image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:16:38.518769image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:16:38.616633image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:16:38.712477image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:16:38.804513image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:16:38.900617image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:16:38.991984image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:16:39.085083image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:16:39.186170image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:16:50.033620image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:16:50.130451image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:16:50.230156image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:16:50.331976image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:16:50.426830image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:16:50.533636image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:16:50.653708image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:16:50.757717image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:16:50.864942image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:17:01.936243image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:17:02.027344image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:17:02.128928image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:17:02.215594image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:17:02.298998image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:17:02.385187image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:17:02.468060image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:17:02.554210image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:17:02.648841image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:17:14.207453image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:17:14.302214image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:17:14.400761image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:17:14.498770image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:17:14.593797image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:17:14.685059image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:17:14.775607image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:17:14.869259image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:17:14.975201image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:17:27.928396image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:17:28.019931image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:17:28.113798image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:17:28.203684image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:17:28.289706image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:17:28.379526image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:17:28.462783image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:17:28.547887image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:17:28.642591image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:17:39.801558image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:17:39.899480image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:17:39.999556image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:17:40.091734image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:17:40.178241image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:17:40.270907image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:17:40.365012image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:17:40.453850image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:17:40.553918image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:17:51.794943image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:17:51.889822image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:17:51.989539image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:17:52.087325image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:17:52.179019image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:17:52.274067image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:17:52.362148image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-14T12:17:52.461685image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-04-14T12:17:55.750077image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-04-14T12:17:55.906917image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-04-14T12:17:56.063875image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-04-14T12:17:56.220809image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2022-04-14T12:17:56.361897image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2022-04-14T12:17:52.626272image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-04-14T12:17:52.847167image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

client_idage_bracketjobmaritaleducationhas_housing_loanhas_personal_loanprev_call_durationnum_contacts_prevcpisubs_depositcontact_monthcontact_daycontact_weekcontact_weekday
041020313.01.01.00.0283.0192.379179281
123720453.02.00.01.0169.0294.215157191
229378311.01.00.00.0552.0093.44411821
336636222.03.01.01.0206.0093.200021167
438229112.01.00.00.0341.0093.075144143
527202231.02.00.00.081.0093.444068235
61409411.01.00.00.01076.0192.893175274
724379351.03.00.00.0133.0093.918067234
810036271.02.00.00.0253.0192.893035101
918115331.01.00.00.0467.0094.46511616

Last rows

client_idage_bracketjobmaritaleducationhas_housing_loanhas_personal_loanprev_call_durationnum_contacts_prevcpisubs_depositcontact_monthcontact_daycontact_weekcontact_weekday
358124211271.03.01.00.0159.0093.918067234
358228348341.02.00.00.077.0093.444078277
35834739271.04.01.00.0418.0092.89301515
35842389273.02.00.00.0827.0092.893175274
358510248222.02.01.00.013.0092.893035101
35867519362.02.01.00.0396.0092.89312561
358729822311.01.01.00.0115.0093.44401821
358824462211.03.01.00.0214.0093.918067234
358926089241.02.01.00.076.0093.91802763
359040631212.01.01.00.0368.0092.379049151